Fine Grained Categorization of an Image using Part Proposals

Ms. Dipti S. S. Borade, Prof. N. M. Shahane


Fine grained image categorization is a challenging
task which aims to distinguish objects belonging to the same
category. There is a need to classify objects of same class with
some differences. General image categorization is comparatively
easier than fine grained image categorization but it may fail
to discriminate objects belonging to same class like birds, cars,
plants etc. The proposed work aims to generate image representation which can be suitable for fine grained categorization. In
the proposed system object proposals are extracted from input
image. From each object proposal, multi-scale part proposals are
generated, from which many useful part proposals are selected.
A global image representation is generated using selected useful
part proposals. The global image representation is then used to
train the classifier for image categorization. Application areas
are forestry, agriculture, industry and research societies.

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